Parallelization of whole genome alignment

With the advent of High performance computing, it is now possible to achieve orders of magnitude performance and computation e ciency gains over conventional computer architectures. This thesis explores the potential of using high performance computing to accelerate whole genome alignment. A parallel technique is applied to an algorithm for whole genome alignment, this technique is explained and some experiments were carried out to test it. This technique is based in a fair usage of the available resource to execute genome alignment and how this can be used in HPC clusters. This work is a rst approximation to whole genome alignment and it shows the advantages of parallelism and some of the drawbacks that our technique has. This work describes the resource limitations of current WGA applications when dealing with large quantities of sequences. It proposes a parallel heuristic to distribute the load and to assure that alignment quality is mantained.

Improving Clustering Algorithms for Image Segmentation using Contour and Region Information

In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach

An information theoretic framework for image segmentation

In this paper, an information theoretic framework for image segmentation is presented. This approach is based on the information channel that goes from the image intensity histogram to the regions of the partitioned image. It allows us to define a new family of segmentation methods which maximize the mutual information of the channel. Firstly, a greedy top-down algorithm which partitions an image into homogeneous regions is introduced. Secondly, a histogram quantization algorithm which clusters color bins in a greedy bottom-up way is defined. Finally, the resulting regions in the partitioning algorithm can optionally be merged using the quantized histogram

Rank estimation of trajectory matrix in motion segmentation

A novel technique for estimating the rank of the trajectory matrix in the local subspace affinity (LSA) motion segmentation framework is presented. This new rank estimation is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built with LSA. The result is an enhanced model selection technique for trajectory matrix rank estimation by which it is possible to automate LSA, without requiring any a priori knowledge, and to improve the final segmentation

Enhanced Model Selection for motion segmentation

In this paper a novel rank estimation technique for trajectories motion segmentation within the Local Subspace Affinity (LSA) framework is presented. This technique, called Enhanced Model Selection (EMS), is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built by LSA. The results on synthetic and real data show that without any a priori knowledge, EMS automatically provides an accurate and robust rank estimation, improving the accuracy of the final motion segmentation

Genome Biology and Evolution of the Animal Kingdom

Estudi realitzat a partir d’una estada a la Institut J.W. Jenkinson Laboratory for Evolution and Development of the University of Oxford, Regne Unit, entre 2010 i 2012. He estat membre del laboratori del Professor Peter W.H. Holland com a becari post-doctoral Beatriu de Pinós des de setembre de 2010 al setembre de 2012. El nostre projecte de recerca se centra en l'anàlisi genòmic comparatiu del Regne Animal, tot explorant el contingut dels genomes a través de totes les branques de l'arbre dels animals. Totes les referències a les meves publicacions durant aquest post-doc es poden trobar a http://about.me/jordi_paps. Crec que el nombre i la qualitat dels resultats del meu post-doc, un total de 8 publicacions incloent dos articles a la prestigiosa revista Nature, són prova de l'èxit d'aquest post-doc. Prof Peter W. H. Holland (Departament de Zoologia de la Universitat d'Oxford) i jo som coautors de tres articles de genòmica comparativa, resultats directes d'aquest projecte: 1) comparació de families gèniques entre vertebrats invertebrats (Briefings in Functional Genomics), 2) el genoma de l'ostra (publicat a la revista Nature), i 3) els genomes de 6 platihelmints paràsits (acceptat també a Nature). A més, tenim altres 2 treballs en preparació. Un d'ells analitza l'evolució, expressió i funció dels gens Hox al a la tènia Hymenolepis. El perfil fi d'aquests gens clau del desenvolupament esclareix els canvis d'estil de vida dels organismes. A més, durant aquest últim post-doc he participat en diverses col•laboracions, incloent anàlisi de gens d'envelliment a cucs plans, un estudi sobre la filogènia del grup Gastrotricha, una revisió de l'evolució phylum Platyhelminthes, així com un capítol d'un llibre sobre l'evolució dels animals bilaterals. Finalment, gràcies a la beca Beatriu de Pinós, el Prof. Peter W.H. Holland m'ha convidat a formar part del seu equip com un investigador post-doctoral en el seu projecte ERC Advance actual sobre duplicacions genòmiques.

Multi-scale label interaction to improve object detection and segmentation

The project aims at advancing the state of the art in the use of context information for classification of image and video data. The use of context in the classification of images has been showed of great importance to improve the performance of actual object recognition systems. In our project we proposed the concept of Multi-scale Feature Labels as a general and compact method to exploit the local and global context. The feature extraction from the discriminative probability or classification confidence label field is of great novelty. Moreover the use of a multi-scale representation of the feature labels lead to a compact and efficient description of the context. The goal of the project has been also to provide a general-purpose method and prove its suitability in different image/video analysis problem. The two-year project generated 5 journal publications (plus 2 under submission), 10 conference publications (plus 2 under submission) and one patent (plus 1 pending). Of these publications, a relevant number make use of the main result of this project to improve the results in detection and/or segmentation of objects.

Whole genome analysis of p38 SAPK-mediated gene expression upon stress

Background: Cells have the ability to respond and adapt to environmental changes through activation of stress-activated protein kinases (SAPKs). Although p38 SAPK signalling is known to participate in the regulation of gene expression little is known on the molecular mechanisms used by this SAPK to regulate stress-responsive genes and the overall set of genes regulated by p38 in response to different stimuli.Results: Here, we report a whole genome expression analyses on mouse embryonic fibroblasts (MEFs) treated with three different p38 SAPK activating-stimuli, namely osmostress, the cytokine TNFα and the protein synthesis inhibitor anisomycin. We have found that the activation kinetics of p38α SAPK in response to these insults is different and also leads to a complex gene pattern response specific for a given stress with a restricted set of overlapping genes. In addition, we have analysed the contribution of p38α the major p38 family member present in MEFs, to the overall stress-induced transcriptional response by using both a chemical inhibitor (SB203580) and p38α deficient (p38α-/-) MEFs. We show here that p38 SAPK dependency ranged between 60% and 88% depending on the treatments and that there is a very good overlap between the inhibitor treatment and the ko cells. Furthermore, we have found that the dependency of SAPK varies depending on the time the cells are subjected to osmostress. Conclusions: Our genome-wide transcriptional analyses shows a selective response to specific stimuli and a restricted common response of up to 20% of the stress up-regulated early genes that involves an important set of transcription factors, which might be critical for either cell adaptation or preparation for continuous extra-cellular changes. Interestingly, up to 85% of the up-regulated genes are under the transcriptional control of p38 SAPK. Thus, activation of p38 SAPK is critical to elicit the early gene expression program required for cell adaptation to stress.

A bi-dimensional genome scan for prolificacy traits in pigs shows the existence of multiple epistatic QTL

Background: Prolificacy is the most important trait influencing the reproductive efficiency of pig production systems. The low heritability and sex-limited expression of prolificacy have hindered to some extent the improvement of this trait through artificial selection. Moreover, the relative contributions of additive, dominant and epistatic QTL to the genetic variance of pig prolificacy remain to be defined. In this work, we have undertaken this issue by performing one-dimensional and bi-dimensional genome scans for number of piglets born alive (NBA) and total number of piglets born (TNB) in a three generation Iberian by Meishan F2 intercross. Results: The one-dimensional genome scan for NBA and TNB revealed the existence of two genome-wide highly significant QTL located on SSC13 (P < 0.001) and SSC17 (P < 0.01) with effects on both traits. This relative paucity of significant results contrasted very strongly with the wide array of highly significant epistatic QTL that emerged in the bi-dimensional genome-wide scan analysis. As much as 18 epistatic QTL were found for NBA (four at P < 0.01 and five at P < 0.05) and TNB (three at P < 0.01 and six at P < 0.05), respectively. These epistatic QTL were distributed in multiple genomic regions, which covered 13 of the 18 pig autosomes, and they had small individual effects that ranged between 3 to 4% of the phenotypic variance. Different patterns of interactions (a × a, a × d, d × a and d × d) were found amongst the epistatic QTL pairs identified in the current work.Conclusions: The complex inheritance of prolificacy traits in pigs has been evidenced by identifying multiple additive (SSC13 and SSC17), dominant and epistatic QTL in an Iberian × Meishan F2 intercross. Our results demonstrate that a significant fraction of the phenotypic variance of swine prolificacy traits can be attributed to first-order gene-by-gene interactions emphasizing that the phenotypic effects of alleles might be strongly modulated by the genetic background where they segregate.

Structured RNAs in the ENCODE selected regions of the human genome

Functional RNA structures play an important role both in the context of noncoding RNA transcripts as well as regulatory elements in mRNAs. Here we present a computational study to detect functional RNA structures within the ENCODE regions of the human genome. Since structural RNAs in general lack characteristic signals in primary sequence, comparative approaches evaluating evolutionary conservation of structures are most promising. We have used three recently introduced programs based on either phylogenetic–stochastic context-free grammar (EvoFold) or energy directed folding (RNAz and AlifoldZ), yielding several thousand candidate structures (corresponding to ∼2.7% of the ENCODE regions). EvoFold has its highest sensitivity in highly conserved and relatively AU-rich regions, while RNAz favors slightly GC-rich regions, resulting in a relatively small overlap between methods. Comparison with the GENCODE annotation points to functional RNAs in all genomic contexts, with a slightly increased density in 3′-UTRs. While we estimate a significant false discovery rate of ∼50%–70% many of the predictions can be further substantiated by additional criteria: 248 loci are predicted by both RNAz and EvoFold, and an additional 239 RNAz or EvoFold predictions are supported by the (more stringent) AlifoldZ algorithm. Five hundred seventy RNAz structure predictions fall into regions that show signs of selection pressure also on the sequence level (i.e., conserved elements). More than 700 predictions overlap with noncoding transcripts detected by oligonucleotide tiling arrays. One hundred seventy-five selected candidates were tested by RT-PCR in six tissues, and expression could be verified in 43 cases (24.6%).

Tandem chimeric transcription as a means to increase protein diversity in the human genome

The “one-gene, one-protein” rule, coined by Beadle and Tatum, has been fundamental to molecular biology. The rule implies that the genetic complexity of an organism depends essentially on its gene number. The discovery, however, that alternative gene splicing and transcription are widespread phenomena dramatically altered our understanding of the genetic complexity of higher eukaryotic organisms; in these, a limited number of genes may potentially encode a much larger number of proteins. Here we investigate yet another phenomenon that may contribute to generate additional protein diversity. Indeed, by relying on both computational and experimental analysis, we estimate that at least 4%–5% of the tandem gene pairs in the human genome can be eventually transcribed into a single RNA sequence encoding a putative chimeric protein. While the functional significance of most of these chimeric transcripts remains to be determined, we provide strong evidence that this phenomenon does not correspond to mere technical artifacts and that it is a common mechanism with the potential of generating hundreds of additional proteins in the human genome.

EGASP: the human ENCODE Genome Annotation Assessment Project

Background: We present the results of EGASP, a community experiment to assess the state-ofthe-art in genome annotation within the ENCODE regions, which span 1% of the human genomesequence. The experiment had two major goals: the assessment of the accuracy of computationalmethods to predict protein coding genes; and the overall assessment of the completeness of thecurrent human genome annotations as represented in the ENCODE regions. For thecomputational prediction assessment, eighteen groups contributed gene predictions. Weevaluated these submissions against each other based on a ‘reference set’ of annotationsgenerated as part of the GENCODE project. These annotations were not available to theprediction groups prior to the submission deadline, so that their predictions were blind and anexternal advisory committee could perform a fair assessment.Results: The best methods had at least one gene transcript correctly predicted for close to 70%of the annotated genes. Nevertheless, the multiple transcript accuracy, taking into accountalternative splicing, reached only approximately 40% to 50% accuracy. At the coding nucleotidelevel, the best programs reached an accuracy of 90% in both sensitivity and specificity. Programsrelying on mRNA and protein sequences were the most accurate in reproducing the manuallycurated annotations. Experimental validation shows that only a very small percentage (3.2%) of the selected 221 computationally predicted exons outside of the existing annotation could beverified.Conclusions: This is the first such experiment in human DNA, and we have followed thestandards established in a similar experiment, GASP1, in Drosophila melanogaster. We believe theresults presented here contribute to the value of ongoing large-scale annotation projects and shouldguide further experimental methods when being scaled up to the entire human genome sequence.

Nematode selenoproteome: the use of the selenocysteine insertion system to decode one codon in an animal genome?

Selenocysteine (Sec) is co-translationally inserted into selenoproteins in response to codon UGA with the help of the selenocysteine insertion sequence (SECIS) element. The number of selenoproteins in animals varies, with humans having 25 and mice having 24 selenoproteins. To date, however, only one selenoprotein, thioredoxin reductase, has been detected in Caenorhabditis elegans, and this enzyme contains only one Sec. Here, we characterize the selenoproteomes of C.elegans and Caenorhabditis briggsae with three independent algorithms, one searching for pairs of homologous nematode SECIS elements, another searching for Cys- or Sec-containing homologs of potential nematode selenoprotein genes and the third identifying Sec-containing homologs of annotated nematode proteins. These methods suggest that thioredoxin reductase is the only Sec-containing protein in the C.elegans and C.briggsae genomes. In contrast, we identified additional selenoproteins in other nematodes. Assuming that Sec insertion mechanisms are conserved between nematodes and other eukaryotes, the data suggest that nematode selenoproteomes were reduced during evolution, and that in an extreme reduction case Sec insertion systems probably decode only a single UGA codon in C.elegans and C.briggsae genomes. In addition, all detected genes had a rare form of SECIS element containing a guanosine in place of a conserved adenosine present in most other SECIS structures, suggesting that in organisms with small selenoproteomes SECIS elements may change rapidly.

Gene finding in the chicken genome

Background: Despite the continuous production of genome sequence for a number of organisms,reliable, comprehensive, and cost effective gene prediction remains problematic. This is particularlytrue for genomes for which there is not a large collection of known gene sequences, such as therecently published chicken genome. We used the chicken sequence to test comparative andhomology-based gene-finding methods followed by experimental validation as an effective genomeannotation method.Results: We performed experimental evaluation by RT-PCR of three different computational genefinders, Ensembl, SGP2 and TWINSCAN, applied to the chicken genome. A Venn diagram wascomputed and each component of it was evaluated. The results showed that de novo comparativemethods can identify up to about 700 chicken genes with no previous evidence of expression, andcan correctly extend about 40% of homology-based predictions at the 5' end.Conclusions: De novo comparative gene prediction followed by experimental verification iseffective at enhancing the annotation of the newly sequenced genomes provided by standardhomology-based methods.